Using support vector regression to model the correlation between the clinical metastases time and gene expression profile for breast cancer

摘要

ObjectiveRecently, the microarray analysis has been an important tool used for studying the cancer type, biological mechanism, and diagnostic biomarkers. There are several machine-learning methods being used to construct the prognostic model based on the microarray data sets. However, most of these previous studies were focused on the supervised classification for predicting the clinical type of patients. In this study, we investigate whether or not the expression level of some significant genes identified can be used to predict the clinical metastases time of patients.